[Deep Learning in Pathology: Applications and Challenges in Ophthalmic Pathology].

Introduction: Deep learning has received increasing attention in recent years and is used in many different areas. Since image analysis is a strength of deep learning, it would be obvious to use it for histopathological questions too. Our goal is to identify possible deep learning approaches from general pathology which could be used in ophthalmic pathology. In addition, the data of the past year will be used to estimate the proportion of potentially interesting cases and the necessary technical effort.

Methods: Firstly, a literature search for deep learning models and their possible applications in the field of pathology was carried out. In order to estimate the potential benefit, technical challenges and feasibility, the number of suitable ophthalmopathology cases in our lab in 2019 for the identified models was determined and put in relation to the resulting amount of data and the scanning time.

Results: We identified 7 areas of particular interest: determination of regions of interest (ROI), classification of histological images in scoring systems, mapping of tumor fractions, differentiation of different types of inflammation, differentiation of various cutaneous tumors, classification of lymphomas and prediction of patient outcome-based on tumor histology. Within one year, a total of 831 cases (43%) would have been suitable for the above models. The creation of whole slide images (WSI) for all histological cases would have required a storage capacity of 630 GB with a scanning time of 35 h.

Conclusion: There are several deep learning approaches which are also interesting for ophthalmic pathology. Most of them would have to be specially trained for the ophthalmopathological aspects. To be able to apply deep learning approaches, it is necessary to have a good IT infrastructure with the possibility to create and permanently store WSI, and this seems to be technically feasible. Future studies should focus on the specific practical implementation of current deep learning possibilities for ophthalmic pathology.